Correlating MALDI and SIMS imaging mass spectrometric datasets of biological tissue surfaces

Imaging mass spectrometry (IMS) is a rapidly evolving tool for combined chemical and spatial analysis of biological tissues. The complexity of the biological data requires various analytical methods to process the raw datasets. In this article, we report on the 'semi-automated' correlation of two imaging MS datasets obtained with secondary ion mass spectrometry (SIMS) and matrix-assisted laser desorption/ionization (MALDI) on the same, single brain tissue sample. Prior to statistical analysis, the raw datasets are preprocessed with novel algorithms for baseline correction and peak picking. Principal component analysis (PCA) and canonical correlation analysis (CCA) are used in concert to extract the maximum amount of information about the location of different biochemical molecules on the tissue surface. More importantly, the results show that combining the information from MALDI and SIMS, by using CCA, enables us to correlate and improve the individual results of these two imaging MS experiments. Copyright (C) 2009 John Wiley & Sons, Ltd.